Pandas Profiling Report

Overview
Reproduction

Dataset statistics

Number of variables	12
Number of observations	1000
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	93.9 KiB
Average record size in memory	96.1 B

Variable types

Numeric	6
Categorical	6

Reproduction

Analysis started	2023-08-19 09:10:24.129493
Analysis finished	2023-08-19 09:10:29.647447
Duration	5.52 seconds
Software version	ydata-profiling v0.0.dev0
Download configuration	config.json

Age
Real number (ℝ)

Distinct	46
Distinct (%)	4.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	42.702

Minimum	20
Maximum	65
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	20
5-th percentile	22
Q1	31
median	42
Q3	54
95-th percentile	64
Maximum	65
Range	45
Interquartile range (IQR)	23

Descriptive statistics

Standard deviation	13.266771
Coefficient of variation (CV)	0.31068265
Kurtosis	-1.2175133
Mean	42.702
Median Absolute Deviation (MAD)	12
Skewness	0.037307478
Sum	42702
Variance	176.0072
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=46)

Value	Count	Frequency (%)
64	31	3.1%
28	28	2.8%
54	28	2.8%
49	27	2.7%
34	27	2.7%
29	27	2.7%
40	26	2.6%
45	25	2.5%
26	25	2.5%
36	25	2.5%
Other values (36)	731	73.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
20	18	1.8%
21	17	1.7%
22	17	1.7%
23	16	1.6%
24	23	2.3%
25	24	2.4%
26	25	2.5%
27	22	2.2%
28	28	2.8%
29	27	2.7%

Value	Count	Frequency (%)
65	20	2.0%
64	31	3.1%
63	24	2.4%
62	22	2.2%
61	23	2.3%
60	25	2.5%
59	22	2.2%
58	18	1.8%
57	17	1.7%
56	22	2.2%

Gender
Categorical

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Male	501
Female	499

Length

Max length	6
Median length	4
Mean length	4.998
Min length	4

Characters and Unicode

Total characters	4998
Distinct characters	6
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Female
4th row	Female
5th row	Male

Common Values

Value	Count	Frequency (%)
Male	501	50.1%
Female	499	49.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
male	501	50.1%
female	499	49.9%

Most occurring characters

Value	Count	Frequency (%)
e	1499	30.0%
a	1000	20.0%
l	1000	20.0%
M	501	10.0%
F	499	10.0%
m	499	10.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3998	80.0%
Uppercase Letter	1000	20.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1499	37.5%
a	1000	25.0%
l	1000	25.0%
m	499	12.5%

Uppercase Letter

Value	Count	Frequency (%)
M	501	50.1%
F	499	49.9%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4998	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1499	30.0%
a	1000	20.0%
l	1000	20.0%
M	501	10.0%
F	499	10.0%
m	499	10.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4998	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1499	30.0%
a	1000	20.0%
l	1000	20.0%
M	501	10.0%
F	499	10.0%
m	499	10.0%

Marital Status
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Married	350
Divorced	326
Single	324

Length

Max length	8
Median length	7
Mean length	7.002
Min length	6

Characters and Unicode

Total characters	7002
Distinct characters	14
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Married
2nd row	Married
3rd row	Single
4th row	Married
5th row	Married

Common Values

Value	Count	Frequency (%)
Married	350	35.0%
Divorced	326	32.6%
Single	324	32.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
married	350	35.0%
divorced	326	32.6%
single	324	32.4%

Most occurring characters

Value	Count	Frequency (%)
r	1026	14.7%
i	1000	14.3%
e	1000	14.3%
d	676	9.7%
M	350	5.0%
a	350	5.0%
D	326	4.7%
v	326	4.7%
o	326	4.7%
c	326	4.7%
Other values (4)	1296	18.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6002	85.7%
Uppercase Letter	1000	14.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
r	1026	17.1%
i	1000	16.7%
e	1000	16.7%
d	676	11.3%
a	350	5.8%
v	326	5.4%
o	326	5.4%
c	326	5.4%
n	324	5.4%
g	324	5.4%

Uppercase Letter

Value	Count	Frequency (%)
M	350	35.0%
D	326	32.6%
S	324	32.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7002	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
r	1026	14.7%
i	1000	14.3%
e	1000	14.3%
d	676	9.7%
M	350	5.0%
a	350	5.0%
D	326	4.7%
v	326	4.7%
o	326	4.7%
c	326	4.7%
Other values (4)	1296	18.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7002	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
r	1026	14.7%
i	1000	14.3%
e	1000	14.3%
d	676	9.7%
M	350	5.0%
a	350	5.0%
D	326	4.7%
v	326	4.7%
o	326	4.7%
c	326	4.7%
Other values (4)	1296	18.5%

Education Level
Categorical

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Master	262
High School	250
PhD	245
Bachelor	243

Length

Max length	11
Median length	8
Mean length	7.001
Min length	3

Characters and Unicode

Total characters	7001
Distinct characters	18
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Master
2nd row	High School
3rd row	Master
4th row	PhD
5th row	Bachelor

Common Values

Value	Count	Frequency (%)
Master	262	26.2%
High School	250	25.0%
PhD	245	24.5%
Bachelor	243	24.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
master	262	21.0%
high	250	20.0%
school	250	20.0%
phd	245	19.6%
bachelor	243	19.4%

Most occurring characters

Value	Count	Frequency (%)
h	988	14.1%
o	743	10.6%
a	505	7.2%
e	505	7.2%
r	505	7.2%
l	493	7.0%
c	493	7.0%
M	262	3.7%
t	262	3.7%
s	262	3.7%
Other values (8)	1983	28.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5256	75.1%
Uppercase Letter	1495	21.4%
Space Separator	250	3.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
h	988	18.8%
o	743	14.1%
a	505	9.6%
e	505	9.6%
r	505	9.6%
l	493	9.4%
c	493	9.4%
t	262	5.0%
s	262	5.0%
g	250	4.8%

Uppercase Letter

Value	Count	Frequency (%)
M	262	17.5%
S	250	16.7%
H	250	16.7%
P	245	16.4%
D	245	16.4%
B	243	16.3%

Space Separator

Value	Count	Frequency (%)
	250	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6751	96.4%
Common	250	3.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
h	988	14.6%
o	743	11.0%
a	505	7.5%
e	505	7.5%
r	505	7.5%
l	493	7.3%
c	493	7.3%
M	262	3.9%
t	262	3.9%
s	262	3.9%
Other values (7)	1733	25.7%

Common

Value	Count	Frequency (%)
	250	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7001	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
h	988	14.1%
o	743	10.6%
a	505	7.2%
e	505	7.2%
r	505	7.2%
l	493	7.0%
c	493	7.0%
M	262	3.7%
t	262	3.7%
s	262	3.7%
Other values (8)	1983	28.3%

Employment Status
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Self-Employed	347
Employed	328
Unemployed	325

Length

Max length	13
Median length	10
Mean length	10.385
Min length	8

Characters and Unicode

Total characters	10385
Distinct characters	13
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Employed
2nd row	Unemployed
3rd row	Employed
4th row	Unemployed
5th row	Self-Employed

Common Values

Value	Count	Frequency (%)
Self-Employed	347	34.7%
Employed	328	32.8%
Unemployed	325	32.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
self-employed	347	34.7%
employed	328	32.8%
unemployed	325	32.5%

Most occurring characters

Value	Count	Frequency (%)
e	1672	16.1%
l	1347	13.0%
m	1000	9.6%
p	1000	9.6%
o	1000	9.6%
y	1000	9.6%
d	1000	9.6%
E	675	6.5%
S	347	3.3%
f	347	3.3%
Other values (3)	997	9.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8691	83.7%
Uppercase Letter	1347	13.0%
Dash Punctuation	347	3.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1672	19.2%
l	1347	15.5%
m	1000	11.5%
p	1000	11.5%
o	1000	11.5%
y	1000	11.5%
d	1000	11.5%
f	347	4.0%
n	325	3.7%

Uppercase Letter

Value	Count	Frequency (%)
E	675	50.1%
S	347	25.8%
U	325	24.1%

Dash Punctuation

Value	Count	Frequency (%)
-	347	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	10038	96.7%
Common	347	3.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1672	16.7%
l	1347	13.4%
m	1000	10.0%
p	1000	10.0%
o	1000	10.0%
y	1000	10.0%
d	1000	10.0%
E	675	6.7%
S	347	3.5%
f	347	3.5%
Other values (2)	650	6.5%

Common

Value	Count	Frequency (%)
-	347	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10385	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1672	16.1%
l	1347	13.0%
m	1000	9.6%
p	1000	9.6%
o	1000	9.6%
y	1000	9.6%
d	1000	9.6%
E	675	6.5%
S	347	3.3%
f	347	3.3%
Other values (3)	997	9.6%

Credit Utilization Ratio
Real number (ℝ)

Distinct	101
Distinct (%)	10.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.50995

Minimum	0
Maximum	1
Zeros	3
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0.06
Q1	0.25
median	0.53
Q3	0.75
95-th percentile	0.96
Maximum	1
Range	1
Interquartile range (IQR)	0.5

Descriptive statistics

Standard deviation	0.29105672
Coefficient of variation (CV)	0.5707554
Kurtosis	-1.2031434
Mean	0.50995
Median Absolute Deviation (MAD)	0.24
Skewness	-0.04789134
Sum	509.95
Variance	0.084714012
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.74	24	2.4%
0.66	18	1.8%
0.96	17	1.7%
0.6	17	1.7%
0.08	16	1.6%
0.45	16	1.6%
0.22	15	1.5%
0.78	15	1.5%
0.71	15	1.5%
0.91	15	1.5%
Other values (91)	832	83.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	3	0.3%
0.01	9	0.9%
0.02	7	0.7%
0.03	6	0.6%
0.04	15	1.5%
0.05	9	0.9%
0.06	13	1.3%
0.07	7	0.7%
0.08	16	1.6%
0.09	5	0.5%

Value	Count	Frequency (%)
1	9	0.9%
0.99	8	0.8%
0.98	12	1.2%
0.97	12	1.2%
0.96	17	1.7%
0.95	14	1.4%
0.94	7	0.7%
0.93	11	1.1%
0.92	9	0.9%
0.91	15	1.5%

Payment History
Real number (ℝ)

Distinct	101
Distinct (%)	10.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1452.814

Minimum	0
Maximum	2857
Zeros	10
Zeros (%)	1.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	142
Q1	763.75
median	1428
Q3	2142
95-th percentile	2742
Maximum	2857
Range	2857
Interquartile range (IQR)	1378.25

Descriptive statistics

Standard deviation	827.93415
Coefficient of variation (CV)	0.5698831
Kurtosis	-1.1841781
Mean	1452.814
Median Absolute Deviation (MAD)	714
Skewness	0.0083047057
Sum	1452814
Variance	685474.95
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2628	17	1.7%
2800	17	1.7%
2085	17	1.7%
514	16	1.6%
857	15	1.5%
1371	15	1.5%
1142	15	1.5%
1714	14	1.4%
485	14	1.4%
2571	14	1.4%
Other values (91)	846	84.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	10	1.0%
28	5	0.5%
57	6	0.6%
85	9	0.9%
114	14	1.4%
142	11	1.1%
171	5	0.5%
200	9	0.9%
228	7	0.7%
257	8	0.8%

Value	Count	Frequency (%)
2857	12	1.2%
2828	7	0.7%
2800	17	1.7%
2771	10	1.0%
2742	9	0.9%
2714	10	1.0%
2685	9	0.9%
2657	11	1.1%
2628	17	1.7%
2600	6	0.6%

Number of Credit Accounts
Real number (ℝ)

Distinct	10
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.58

Minimum	1
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	6
Q3	8
95-th percentile	10
Maximum	10
Range	9
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	2.9336336
Coefficient of variation (CV)	0.52574079
Kurtosis	-1.2516581
Mean	5.58
Median Absolute Deviation (MAD)	3
Skewness	-0.052238532
Sum	5580
Variance	8.6062062
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
8	117	11.7%
10	116	11.6%
1	113	11.3%
7	106	10.6%
3	102	10.2%
4	98	9.8%
5	89	8.9%
9	88	8.8%
6	88	8.8%
2	83	8.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	113	11.3%
2	83	8.3%
3	102	10.2%
4	98	9.8%
5	89	8.9%
6	88	8.8%
7	106	10.6%
8	117	11.7%
9	88	8.8%
10	116	11.6%

Value	Count	Frequency (%)
10	116	11.6%
9	88	8.8%
8	117	11.7%
7	106	10.6%
6	88	8.8%
5	89	8.9%
4	98	9.8%
3	102	10.2%
2	83	8.3%
1	113	11.3%

Loan Amount
Real number (ℝ)

Distinct	897
Distinct (%)	89.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2471401

Minimum	108000
Maximum	4996000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	108000
5-th percentile	329000
Q1	1298000
median	2437500
Q3	3653250
95-th percentile	4634550
Maximum	4996000
Range	4888000
Interquartile range (IQR)	2355250

Descriptive statistics

Standard deviation	1387046.7
Coefficient of variation (CV)	0.56123902
Kurtosis	-1.1585383
Mean	2471401
Median Absolute Deviation (MAD)	1162000
Skewness	0.040700646
Sum	2.471401 × 10⁹
Variance	1.9238985 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
149000	3	0.3%
2604000	3	0.3%
2684000	3	0.3%
2283000	3	0.3%
2862000	3	0.3%
2081000	3	0.3%
3266000	3	0.3%
2759000	3	0.3%
1209000	2	0.2%
2169000	2	0.2%
Other values (887)	972	97.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
108000	1	0.1%
109000	1	0.1%
118000	1	0.1%
127000	1	0.1%
134000	1	0.1%
135000	1	0.1%
143000	1	0.1%
149000	3	0.3%
151000	1	0.1%
155000	1	0.1%

Value	Count	Frequency (%)
4996000	1	0.1%
4988000	1	0.1%
4970000	1	0.1%
4963000	1	0.1%
4959000	1	0.1%
4956000	1	0.1%
4948000	1	0.1%
4946000	1	0.1%
4933000	1	0.1%
4932000	1	0.1%

Interest Rate
Real number (ℝ)

Distinct	774
Distinct (%)	77.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.6866

Minimum	1.01
Maximum	19.99
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1.01
5-th percentile	2.1685
Q1	6.0225
median	10.705
Q3	15.44
95-th percentile	19.12
Maximum	19.99
Range	18.98
Interquartile range (IQR)	9.4175

Descriptive statistics

Standard deviation	5.4790577
Coefficient of variation (CV)	0.51270355
Kurtosis	-1.1851318
Mean	10.6866
Median Absolute Deviation (MAD)	4.72
Skewness	-0.020068822
Sum	10686.6
Variance	30.020074
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.47	4	0.4%
4.38	4	0.4%
6.03	4	0.4%
4.35	4	0.4%
9.05	4	0.4%
15.28	4	0.4%
15.58	3	0.3%
5.19	3	0.3%
13.96	3	0.3%
11.39	3	0.3%
Other values (764)	964	96.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1.01	1	0.1%
1.06	1	0.1%
1.07	1	0.1%
1.1	1	0.1%
1.11	1	0.1%
1.12	2	0.2%
1.13	1	0.1%
1.14	2	0.2%
1.15	1	0.1%
1.17	1	0.1%

Value	Count	Frequency (%)
19.99	1	0.1%
19.98	1	0.1%
19.96	1	0.1%
19.93	1	0.1%
19.92	2	0.2%
19.88	1	0.1%
19.86	3	0.3%
19.85	2	0.2%
19.83	1	0.1%
19.82	1	0.1%

Loan Term
Categorical

Distinct	5
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

60	234
48	205
12	199
36	181
24	181

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	2000
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	48
2nd row	60
3rd row	12
4th row	60
5th row	36

Common Values

Value	Count	Frequency (%)
60	234	23.4%
48	205	20.5%
12	199	19.9%
36	181	18.1%
24	181	18.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
60	234	23.4%
48	205	20.5%
12	199	19.9%
36	181	18.1%
24	181	18.1%

Most occurring characters

Value	Count	Frequency (%)
6	415	20.8%
4	386	19.3%
2	380	19.0%
0	234	11.7%
8	205	10.2%
1	199	10.0%
3	181	9.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2000	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
6	415	20.8%
4	386	19.3%
2	380	19.0%
0	234	11.7%
8	205	10.2%
1	199	10.0%
3	181	9.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2000	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
6	415	20.8%
4	386	19.3%
2	380	19.0%
0	234	11.7%
8	205	10.2%
1	199	10.0%
3	181	9.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2000	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
6	415	20.8%
4	386	19.3%
2	380	19.0%
0	234	11.7%
8	205	10.2%
1	199	10.0%
3	181	9.0%

Type of Loan
Categorical

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Auto Loan	348
Home Loan	328
Personal Loan	324

Length

Max length	13
Median length	9
Mean length	10.296
Min length	9

Characters and Unicode

Total characters	10296
Distinct characters	15
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Personal Loan
2nd row	Auto Loan
3rd row	Auto Loan
4th row	Auto Loan
5th row	Personal Loan

Common Values

Value	Count	Frequency (%)
Auto Loan	348	34.8%
Home Loan	328	32.8%
Personal Loan	324	32.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
loan	1000	50.0%
auto	348	17.4%
home	328	16.4%
personal	324	16.2%

Most occurring characters

Value	Count	Frequency (%)
o	2000	19.4%
a	1324	12.9%
n	1324	12.9%
	1000	9.7%
L	1000	9.7%
e	652	6.3%
A	348	3.4%
u	348	3.4%
t	348	3.4%
H	328	3.2%
Other values (5)	1624	15.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7296	70.9%
Uppercase Letter	2000	19.4%
Space Separator	1000	9.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	2000	27.4%
a	1324	18.1%
n	1324	18.1%
e	652	8.9%
u	348	4.8%
t	348	4.8%
m	328	4.5%
r	324	4.4%
s	324	4.4%
l	324	4.4%

Uppercase Letter

Value	Count	Frequency (%)
L	1000	50.0%
A	348	17.4%
H	328	16.4%
P	324	16.2%

Space Separator

Value	Count	Frequency (%)
	1000	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9296	90.3%
Common	1000	9.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	2000	21.5%
a	1324	14.2%
n	1324	14.2%
L	1000	10.8%
e	652	7.0%
A	348	3.7%
u	348	3.7%
t	348	3.7%
H	328	3.5%
m	328	3.5%
Other values (4)	1296	13.9%

Common

Value	Count	Frequency (%)
	1000	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10296	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	2000	19.4%
a	1324	12.9%
n	1324	12.9%
	1000	9.7%
L	1000	9.7%
e	652	6.3%
A	348	3.4%
u	348	3.4%
t	348	3.4%
H	328	3.2%
Other values (5)	1624	15.8%

Auto

Heatmap
Table

	Age	Credit Utilization Ratio	Payment History	Number of Credit Accounts	Loan Amount	Interest Rate	Gender	Marital Status	Education Level	Employment Status	Loan Term	Type of Loan
Age	1.000	0.000	-0.003	-0.049	0.035	0.032	0.000	0.000	0.034	0.056	0.045	0.070
Credit Utilization Ratio	0.000	1.000	0.004	-0.001	-0.012	0.048	0.000	0.083	0.010	0.000	0.000	0.000
Payment History	-0.003	0.004	1.000	0.024	-0.020	-0.016	0.000	0.056	0.000	0.000	0.041	0.056
Number of Credit Accounts	-0.049	-0.001	0.024	1.000	0.032	-0.002	0.000	0.000	0.000	0.023	0.000	0.033
Loan Amount	0.035	-0.012	-0.020	0.032	1.000	0.047	0.042	0.018	0.000	0.000	0.000	0.000
Interest Rate	0.032	0.048	-0.016	-0.002	0.047	1.000	0.000	0.000	0.000	0.017	0.024	0.000
Gender	0.000	0.000	0.000	0.000	0.042	0.000	1.000	0.000	0.041	0.000	0.036	0.000
Marital Status	0.000	0.083	0.056	0.000	0.018	0.000	0.000	1.000	0.000	0.000	0.034	0.000
Education Level	0.034	0.010	0.000	0.000	0.000	0.000	0.041	0.000	1.000	0.000	0.019	0.000
Employment Status	0.056	0.000	0.000	0.023	0.000	0.017	0.000	0.000	0.000	1.000	0.029	0.000
Loan Term	0.045	0.000	0.041	0.000	0.000	0.024	0.036	0.034	0.019	0.029	1.000	0.000
Type of Loan	0.070	0.000	0.056	0.033	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

	Age	Gender	Marital Status	Education Level	Employment Status	Credit Utilization Ratio	Payment History	Number of Credit Accounts	Loan Amount	Interest Rate	Loan Term	Type of Loan
0	60	Male	Married	Master	Employed	0.22	2685.0	2	4675000	2.65	48	Personal Loan
1	25	Male	Married	High School	Unemployed	0.20	2371.0	9	3619000	5.19	60	Auto Loan
2	30	Female	Single	Master	Employed	0.22	2771.0	6	957000	2.76	12	Auto Loan
3	58	Female	Married	PhD	Unemployed	0.12	1371.0	2	4731000	6.57	60	Auto Loan
4	32	Male	Married	Bachelor	Self-Employed	0.99	828.0	2	3289000	6.28	36	Personal Loan
5	42	Male	Divorced	Master	Unemployed	0.94	2342.0	2	1536000	11.15	24	Personal Loan
6	44	Female	Divorced	Bachelor	Unemployed	0.32	2800.0	1	2021000	16.61	36	Auto Loan
7	24	Male	Divorced	Master	Employed	0.66	1428.0	8	1298000	6.03	24	Home Loan
8	54	Female	Divorced	PhD	Unemployed	0.40	800.0	3	4374000	10.38	12	Personal Loan
9	60	Male	Divorced	PhD	Unemployed	0.06	1371.0	10	4026000	11.05	60	Personal Loan

	Age	Gender	Marital Status	Education Level	Employment Status	Credit Utilization Ratio	Payment History	Number of Credit Accounts	Loan Amount	Interest Rate	Loan Term	Type of Loan
990	48	Female	Single	High School	Self-Employed	0.56	257.0	6	4161000	12.50	48	Auto Loan
991	56	Male	Divorced	Bachelor	Self-Employed	0.67	2428.0	3	2283000	16.12	36	Auto Loan
992	25	Female	Single	Bachelor	Unemployed	0.49	2800.0	4	1609000	17.65	12	Personal Loan
993	46	Female	Divorced	PhD	Unemployed	0.10	114.0	7	301000	14.11	12	Personal Loan
994	53	Female	Single	Master	Employed	0.40	2028.0	4	2561000	3.26	36	Home Loan
995	59	Male	Divorced	High School	Employed	0.74	1285.0	8	3530000	12.99	48	Auto Loan
996	64	Male	Divorced	Bachelor	Unemployed	0.77	1857.0	2	1377000	18.02	60	Home Loan
997	63	Female	Single	Master	Self-Employed	0.18	2628.0	10	2443000	18.95	12	Personal Loan
998	51	Female	Married	PhD	Self-Employed	0.32	1142.0	3	1301000	1.80	24	Auto Loan
999	37	Female	Married	Master	Self-Employed	0.17	1028.0	5	4182000	9.34	24	Auto Loan

Overview

Variables

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories